research-article

On the structure and characteristics of user agent string

Authors:

Joel SommersAuthors Info & Claims

IMC '17: Proceedings of the 2017 Internet Measurement Conference

Pages 184 - 190

https://doi.org/10.1145/3131365.3131406

Published: 01 November 2017 Publication History

Abstract

User agent (UA) strings transmitted during HTTP transactions convey client system configuration details to ensure that content returned by a server is appropriate for the requesting host. As such, analysis of UA strings and their structure offers a unique perspective on active client systems in the Internet and when tracked longitudinally, offers a perspective on the nature of system and configuration dynamics. In this paper, we describe our study of UA string characteristics. Our work is based on analyzing a unique corpus of over 1B UA strings collected over a period of 2 years by comScore. We begin by analyzing the general characteristics of UA strings, focusing on the most prevalent strings and dynamic behaviors. We identify the top 10 most popular User Agents, which account for 26% of total daily volume. These strings describe the expected instances of popular platforms such as Microsoft, Apple and Google. We then report on the characteristics of low-volume UA strings, which has important implications for unique device identification. We show that this class of user agent generates the overwhelming majority of traffic, with between 2M and 10M instances observed each day. We show that the distribution of UA strings has temporal dependence and we show the distribution measured depends on the type of content served. Finally, we report on two large-scale UA anomalies characterized by web browsers sending false and misleading UAs in their web requests.

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Cited By

Atique EAlam SAhmad HQazi IQazi ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Uncovering the Hidden Data Costs of Mobile YouTube Video AdsProceedings of the ACM Web Conference 202410.1145/3589334.3645496(1138-1148)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645496
Senol AAcar GKnijnenburg BPapadimitratos P(2023)Unveiling the Impact of User-Agent Reduction and Client Hints: A Measurement StudyProceedings of the 22nd Workshop on Privacy in the Electronic Society10.1145/3603216.3624965(91-106)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3603216.3624965
Mehanović DDurmić N(2022)Case Study Application of Business Intelligence in Digital AdvertisingInternational Journal of E-Business Research10.4018/IJEBR.29329418:1(1-16)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.4018/IJEBR.293294
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Index Terms

On the structure and characteristics of user agent string
1. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Web mining
      1. Traffic analysis
      2. Web log analysis
2. Software and its engineering
  1. Software notations and tools
    1. Context specific languages

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cover image ACM Conferences

IMC '17: Proceedings of the 2017 Internet Measurement Conference

November 2017

509 pages

ISBN:9781450351188

DOI:10.1145/3131365

General Chairs:
Steve Uhlig
Queen Mary University of London
,
Olaf Maennel
Tallinn University of Technology

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 November 2017

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IMC '17

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IMC '17: Internet Measurement Conference

November 1 - 3, 2017

London, United Kingdom

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Cited By

Atique EAlam SAhmad HQazi IQazi ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Uncovering the Hidden Data Costs of Mobile YouTube Video AdsProceedings of the ACM Web Conference 202410.1145/3589334.3645496(1138-1148)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645496
Senol AAcar GKnijnenburg BPapadimitratos P(2023)Unveiling the Impact of User-Agent Reduction and Client Hints: A Measurement StudyProceedings of the 22nd Workshop on Privacy in the Electronic Society10.1145/3603216.3624965(91-106)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3603216.3624965
Mehanović DDurmić N(2022)Case Study Application of Business Intelligence in Digital AdvertisingInternational Journal of E-Business Research10.4018/IJEBR.29329418:1(1-16)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.4018/IJEBR.293294
Passell EStrong RRutter LKim HScheuer LMartini PGrinspoon LGermine L(2021)Cognitive test scores vary with choice of personal digital deviceBehavior Research Methods10.3758/s13428-021-01597-353:6(2544-2557)Online publication date: 5-May-2021
https://doi.org/10.3758/s13428-021-01597-3
Ma ZAustgen JMason JDurumeric ZBailey MLevin DMislove AAmann JLuckie M(2021)Tracing your rootsProceedings of the 21st ACM Internet Measurement Conference10.1145/3487552.3487813(179-194)Online publication date: 2-Nov-2021
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Marquardt FBuhl L(2021)Déjà Vu? Client-Side Fingerprinting and Version Detection of Web Application Software2021 IEEE 46th Conference on Local Computer Networks (LCN)10.1109/LCN52139.2021.9524885(81-89)Online publication date: 4-Oct-2021
https://doi.org/10.1109/LCN52139.2021.9524885
Shinde VDhotre SGavde VDalvi AKazi FBhirud S(2021)CrawlBot: A Domain-Specific Pseudonymous CrawlerCybersecurity in Emerging Digital Era10.1007/978-3-030-84842-2_7(89-101)Online publication date: 14-Aug-2021
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Decanio SSoltys MHildreth K(2020)Voyager: Tracking with a ClickProcedia Computer Science10.1016/j.procs.2020.08.011176(98-107)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.08.011
Vargas SGoel USteiner MBalasubramanian AUnknown (2019)Characterizing JSON Traffic Patterns on a CDNProceedings of the Internet Measurement Conference on - IMC '1910.1145/3355369.3355594(195-201)Online publication date: 2019
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Chen JGou GXiong G(2019)An Analysis of Anomalous User Agent Strings in Network Traffic2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2019.00243(1771-1778)Online publication date: Aug-2019
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